open access publication

Article, 2023

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Annals of Oncology, ISSN 1569-8041, 0923-7534, Volume 35, 1, Pages 29-65, 10.1016/j.annonc.2023.10.125

Contributors

Prelaj, Arsela 0000-0002-3863-088X [1] [2] [3] Miskovic, Vanja 0000-0003-1475-0243 [2] [3] Zanitti, Michele [4] Trovò, Francesco [2] Genova, Carlo 0000-0003-3690-8582 [5] [6] Viscardi, Giuseppe 0000-0003-4473-9387 [7] Rebuzzi, Sara Elena 0000-0003-0546-6304 [6] [8] Mazzeo, Laura 0000-0001-9226-8861 [2] [3] Provenzano, Leonardo [3] Kosta, Sokol 0000-0002-9441-4508 [4] Favali, M [2] Spagnoletti, Andrea 0000-0002-5293-1849 [3] Castelo-Branco, Luis [1] [9] Dolezal, J [10] Pearson, A T [10] Lo Russo, Giuseppe 0000-0003-3224-2728 [3] Proto, Claudia 0000-0003-0287-9787 [3] Ganzinelli, Monica 0000-0002-0526-0835 [3] Giani, Claudia [3] Ambrosini, Emilia 0000-0002-6527-0779 [2] Turajlic, Samra- 0000-0001-8846-136X [11] Au, Lewis 0000-0001-5877-8657 [12] [13] [14] Koopman, M [1] [15] Delaloge, S [1] [16] Kather, Jakob Nikolas 0000-0002-3730-5348 [17] de Braud, F [3] Garassino, M C [10] Pentheroudakis, George E [1] Spencer, C [11] Pedrocchi, Alessandra Laura Giulia 0000-0001-9957-2786 [2]

Affiliations

  1. [1] European Society for Medical Oncology
    2. [NORA names: Switzerland; Europe, Non-EU; OECD];
  2. [2] Politecnico di Milano
    3. [NORA names: Italy; Europe, EU; OECD];
  3. [3] Fondazione IRCCS Istituto Nazionale dei Tumori
    4. [NORA names: Italy; Europe, EU; OECD];
  4. [4] Aalborg University
    5. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
  5. [5] Ospedale Policlinico San Martino
    6. [NORA names: Italy; Europe, EU; OECD];

Abstract

BACKGROUND: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Keywords

AI approaches, AI-based methodologies, AI-based methods, AI-based prediction, Pathom, algorithm, analysis, approach, article, artificial intelligence, benefits, biomarker discovery, biomarkers, cancer, cancer patients, cancer types, changes, checkpoint inhibitors, clinical practice, complex algorithms, data, data modalities, dataset, deep learning, design, development, digital pathology, discovery, efficacy prediction, epigenome, evidence, exploitation, genome, hoc analysis, horizon, immune checkpoint inhibitors, immuno-oncology, immunotherapy, inhibitors, integration, intelligence, learning, lifecycle, lifecycle steps, lung cancer, machine learning, markers, melanoma, method, methodology, modalities, molecular biomarkers, multimodal data, multimodality, multiple cancer types, non-small-cell lung cancer, novel biomarkers, oncological data, original articles, pan-cancer study, pathology, patients, peer-reviewed original articles, post, post hoc analysis, power, practice, practice change, precision immuno-oncology, prediction, predictive biomarker discovery, predictive of benefit, prospective study design, prospective trial design, radiomics, real world, research, review, revolutionised treatment, software, standard machine learning, steps, study, study design, systematic literature review, systematic review, transcriptome, treatment of multiple cancer types, trial design, type, use, validity, widespread use

Funders

  • Cancer Research UK

Data Provider: Digital Science

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